The purpose of this project is to accurately predict rental bike demand on each day on factors such as weather and holiday season. This will help the public lessen the waiting time and enhance the comfort in urban cities. Several other factors such as workday and holiday also affect bike demand other than the weather conditions and therefore, we’re interested in how integrating different variables will change the prediction accuracy. Accurately predicting the bike counts required for stable supply is a major concern as convenience plays a crucial role in people continuing using the services. Therefore, the project aims to predict demand for bike rental demands during am hours and pm hours as well as hourly and daily demands. This will help rental companies to have an idea of how many bikes should be available to meet the demand of the public on any given day. It will also help the company schedule bike maintenance times just in time for the demand.

The trend of monthly interstate traffic volume changes across the years between 2012 and 2018 is analysed and then forecasted for the next 2 years.

Using Linear Regression Model To Predict Fuel Economy using Price

The aim of this investigation is to determine which major supermarket, Coles or Woolworths is cheaper. The sample data of products available from both stores was collected and perform hypothesis testing to determine if there is a statistically significant difference between prices between stores.